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dc.contributor.authorPetridis, Nikolaos E.-
dc.contributor.authorPetridis, Konstantinos-
dc.contributor.authorStiakakis, Emmanouil-
dc.date.accessioned2021-02-06T18:30:12Z-
dc.date.available2021-02-06T18:30:12Z-
dc.date.issued2020-07-
dc.identifier10.1016/j.resconrec.2020.104742en_US
dc.identifier.issn0921-3449en_US
dc.identifier.urihttps://doi.org/10.1016/j.resconrec.2020.104742en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/866-
dc.description.abstractIn this paper, a study for Waste Electrical and Electronic Equipment (WEEE) trade is conducted by using graph theory. In specific, exports and imports for UN COMTRADE data code 854810 which corresponds to waste and scrap of prim cell are collected for 175 countries around the world, spanning the period from 2002 to 2014. WEEE trade networks are generated for each year and communities are produced applying spinglass community detection algorithm. Communities are compared with groups of countries produced by applying detection community algorithms on networks based on common currency, differences in CO2 levels, geographical distances, common language, colonial ties, and regional trade agreements (RTA). An estimation of the factors that affect key network metrics has also been conducted, using a random effect linear regression. The model assesses the effect that economic, environmental, geographical, and social, as well as intra-country commercial agreements have on degree of nodes, betweenness score, and clustering coefficient. The results indicate that communities of WEEE trade network are very similar with groups produced by clustering countries regarding CO2 emissions and distance. Distance, contiguity, common currency, colonial ties, common language, and differences in CO2 levels tend to affect significantly the degree of countries engaged in WEEE trade network. Betweenness score is affected only by common currency while clustering coefficient by common language and CO2 levels between countries. A statistical validation of WEEE network, with Erdos – Renyi, Small – World and Scale – Free networks, was conducted. The results reveal that in, cycle, and middle clustering coefficients of Erdos – Renyi and Small – World networks were statistically equal to the corresponding of WEEE network for the period 2004–2008, while Scale – Free's out and clustering coefficients coincided with WEEE's across all years.en_US
dc.language.isoenen_US
dc.sourceResources, Conservation and Recyclingen_US
dc.subjectFRASCATI::Social sciences::Economics and Business::Econometricsen_US
dc.subjectFRASCATI::Social sciences::Economics and Business::Economicsen_US
dc.subject.othere-Waste tradeen_US
dc.subject.otherGraph theoryen_US
dc.subject.otherNormalized mutual informationen_US
dc.subject.otherMixed effect linear modelsen_US
dc.subject.otherBootstrapen_US
dc.titleGlobal e-waste trade network analysisen_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.volume158en_US
local.identifier.firstpage104742en_US
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